2023
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Risultati di ricerca
I place an emphasis on exploring simple and baseline approaches to gain insights into the workings of deep learning. Until now, my top-5 publications are Self-Cure Network (CVPR-2020), ContrastiveCrop (CVPR-2022 Oral), CAFE (CVPR-2022), DATM (ICLR-2024), and InfoBatch (ICLR-2024 Oral).
Follow. Kai Wang. Kai Wang is currently an assistant professor at the Department of Data and Business Intelligence, Antai College of Economics and Management, Shanghai Jiao Tong University. Before joining SJTU, he was a Research Associate in the Data and Knowledge Research Group at University of New South Wales from 2020 to 2022.
- UNSW
Please check here for more details. Contact me: kaiwang.gua@gmail.com. Office: Gatech CODA building room S1309. I am an assistant professor at Georgia Tech CSE. I received my Ph.D. in Computer Science from Harvard University under supervision of Professor Milind Tambe. My research focuses on developing artificial intelligence, machine learning ...
Cited by. Year. Region attention networks for pose and occlusion robust facial expression recognition. K Wang, X Peng, J Yang, D Meng, Y Qiao. IEEE Transactions on Image Processing 29, 4057-4069. , 2020. 687. 2020. Suppressing uncertainties for large-scale facial expression recognition.
Spectral photonic lattices with complex long-range coupling. BA Bell, K Wang, AS Solntsev, DN Neshev, AA Sukhorukov, BJ Eggleton. Optica 4 (11), 1433-1436. , 2017. 110. 2017. Nontrivial point-gap topology and non-Hermitian skin effect in photonic crystals. J Zhong, K Wang, Y Park, V Asadchy, CC Wojcik, A Dutt, S Fan.
K Wang, B Wilder, A Perrault, M Tambe. Advances in Neural Information Processing Systems 33, 9586-9596. , 2020. 27. 2020. Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning. K Wang, S Shah, H Chen, A Perrault, F Doshi-Velez, M Tambe. arXiv preprint arXiv:2106.03279.
Kai Wang, Bryan Wilder, and Milind Tambe (IJCAI 2019 Doctoral Consortium) 2. Improving GP-UCB Algorithm by Harnessing Decomposed Feedback Kai Wang, Bryan Wilder, Sze-chuan Suen, Milind Tambe, and Bistra Dilkina (ECML 2019 SoGood Workshop; also appeared in the book of “Machine Learning and Knowledge Discovery in Databases”, in proceedings) 1.
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